arXiv (All)
open access
Capturing In-Context Learning Dynamics with Task Operators
In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requires processing the full set of examples, resulting in inefficient deployments, and how ICL works mechanistically is not fully understood. Prior work co
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DIP: Dynamic In-Context Planner For Diffusion Language Models#792685
- DIP: Dynamic In-Context Planner For Diffusion Language Models#788685
- Long-Context Demonstration Selection Using State Space Models#1011603
- T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs#1110139
- Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling#999895
Record · ID 1155989
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive